tbuckley/cabot-search huggingface dataset:
Differentiating ensartinib from lorlatinib and alectinib for first line use in an ALK+ non-small cell lung cancer preclinical model (ResCu).
Authors: Manasa Gadde, Lindsay Hill, Elizabeth Schwabe, Christopher Bulow
Journal: Journal of Clinical Oncology (2023)

e20607 Background: The objective of this study was to determine whether Ensartinib with its high potency and resistance suppressing properties creates a more favorable treatment profile when used as first-line therapy in ALK+ non-small cell lung cancer compared to two other ALK inhibitors, Alectinib and Lorlatinib. Methods: The ResCu system, a novel culturing system, allows for long term culture of cells without the need for passaging, allowing determination of treatment resistance that can develop over time in a physiologically relevant system that conserves the resistance pathways found in patients. Two ALK-dependent lung adenocarcinoma cell lines, H3122 and H2228 were used to derive starting populations for this study. Using ResCu, we drove resistance to three ALK inhibitors: alectinib, lorlatinib, and ensartinib by using a clinically equivalent dose. The evolved cells were characterized for cross-sensitivity and resistance to a representative panel of 21 drugs. Cell viability was assessed by luminescence after 7 days of exposure to these secondary drugs. Additionally, BulkRNA Seq and WES were performed on the resistant evolved cells. Results: The ResCu system developed resistant cell populations by evolutionary steering of H3122 and H2228 cell lines treated with ensartinib, alectinib, and lorlatinib. Genetic background had a significant effect on which ALKi exhibited the most durable response. The H2228 derived cell population became resistant to alectinib and lorlatinib by 14 days while ensartinib remained effective for at least 42 days. In contrast, H3122 derived cells became resistant to all ALK inhibitors tested by 14 days. Rate of cross-resistance generation also varied between ALK inhibitors. Alectinib led to the highest levels of cross-resistance to other ALKi, approximately 3x higher. Lorlatinib led to highest levels of cross-resistance to other drug classes. Treatment with lorlatinib led to resistance in 13 other drug classes. Ensartinib led to least cross-resistance to ALKi and other drug classes. While the H3122 derived cells more rapidly evolved ALKi resistance, first line treatment with ensartinib sensitized them to lorlatinib, extending overall response to ALKi. Conclusions: The ResCu system developed two ALKi resistant cell populations from distinct genetic backgrounds under physiologically relevant conditions. In one of the two genetic background ensartinib response was durable for greater than 42 days compared to 14 days with alectinib and lorlatinib. Alectinib led to the most cross-resistance to other ALKi while lorlatinib led to the most cross-resistance to other drug classes in both backgrounds. Biomarkers of ALKi escape and cross-resistance were derived from RNA-seq, and we are currently validating these alterations against clinical samples.
Barriers to and facilitators of prostate cancer screening among men in Uganda prisons: Findings from a survey of 2565 prisoners.
Authors: Jackson Orem, Innocent Atuhe, Lyn Atuyambe, Alfred Jatho, Babra Nalwadda, Annabella Habinka Ejiri
Journal: Journal of Clinical Oncology (2023)

e18637 Background: Studies have shown that prostate cancer (PCa) is increasing at a rate of 5.2% per annum and as few as 5% of men have ever screened for PCa in Uganda. The situation may be worse among male prisoners given their vulnerability status. The study aimed to examine the perceptions, attitudes and beliefs of men in Uganda Prisons regarding barriers to and facilitators of PCa screening. This would enable the identification of potential interventional strategies to promote PCa screening among men in Uganda prisons. Methods: In May 2022, we conducted 20 focus group discussions and 17 key informant interviews. Results: The belief that all cancers have no cure was one barrier that inhibited most participants not to consider screening as of any value, coupled with fear of being told that one is positive and the stress that comes with it. Participants revealed that they would rather live without getting to know the unknown. In addition, lack of knowledge on prostate cancer and lack of Pca screening services in prisons were perceived as barriers to PCa screening in prison settings. Overall, qualitatively, the interventional strategies that men in Uganda prisons believed could help health care providers promote and increase PCa screening were; using the prison peer educators, working with the UPS to train &amp; equip Prison HCs to screen for PCa, conducting outreach from the cancer hospital, Ministry of Health to work with the UPS authority to establish system for prostate cancer screening in prisons, using health workers within prisons, using the regional prisons health centers to establish prostate cancer screening in prisons, using IEC materials such as posters, and conducting group health education sessions. Conclusions: Culturally-relevant interventions are needed to address the barriers to prostate cancer screening among men in the Ugandan prisons focusing at the entire prison health system.
Key components of goals of care conversations on determining end-of-life care for advanced cancer patients.
Authors: Amy An, Amina P Alio, Ronald M. Epstein, Christopher Seplaki
Journal: Journal of Clinical Oncology (2023)

12130 Background: Early goals of care (GOC) conversations appear to improve end of life (EOL) care in patients with advanced cancer. However, interventions to facilitate GOC conversations may be unsuccessful in changing EOL outcomes due to incomplete understanding of the components of such conversations essential to impacting EOL care. This study seeks to identify the specific components of GOC conversations between patients and oncologists that are associated with less aggressive EOL care. Methods: This was a secondary, sequential mixed-methods analysis of data collected as part of the Values and Options in Cancer Care (VOICE) study, an RCT to improve communication between patients and oncologists. We used qualitative methods to describe and quantify components of GOC conversations occurring in recorded oncology office visits. We then conducted logistic regression analyses to examine the associations between key components identified with less aggressive EOL care. Results: 107 patients who died within 1 year of the recorded visit were included for analysis. Content related to GOC conversations could be found in 80.4% of visits. Most commonly discussed content was related to treatment (72.9%), followed by prognosis (43.9%), EOL care (17.8%), and patient values and preferences (16.8%). In adjusted analyses, discussion of treatment expectation, offer of an alternative to cancer-directed treatment, and mention of hospice were significantly associated with less aggressive EOL care, OR 4.66 (p = 0.047), OR 6.09 (p = 0.008), and OR 7.60 (p = 0.046) respectively. We did not find significant association between discussion of life expectancy and EOL care. Conclusions: GOC conversations may be more effective at impacting EOL care when it includes discussion regarding treatment expectations and offer of an alternative option to cancer-directed therapy, such as hospice.
Ethnic disparity in hospitalization among veterans with COVID-19 and cancer.
Authors: Maya Gogtay, Ruchi Kaushik, Nicolina Martinez, Michael Mader, Sandra Sanchez-Reilly, Marcos I. Restrepo
Journal: Journal of Clinical Oncology (2023)

e18613 Background: COVID19 results in severe disease and resource utilization, particularly among patients with cancer. Hospitalization rates among COVID19 outpatient US veterans with cancer vary according to race (white vs non-Hispanic black). Less is known about ethnic (Hispanic vs non-Hispanic) disparities in hospitalization rates. We aimed to identify ethnic disparities in all-cause hospitalization rates among COVID19 outpatient US veterans with cancer. Methods: We conducted a retrospective cohort study of veterans with cancer and an index diagnosis of COVID19 (ICD10 U07) between 1/1/2020 and 3/31/2022 managed at the South Texas Veterans Health Care System. We included patients for whom ethnicity data (Hispanic or non-Hispanic white) were available. Our primary outcome was all-cause hospitalization rates at 30 days and 60 days following an index diagnosis of COVID19 among Hispanic and non-Hispanic white patients with cancer. We analyzed continuous variables using unpaired t test and categorical variables using Chi-square analysis for statistical significance (p &lt; 0.05). Results: Of 968 outpatient veterans with a history of cancer and a COVID19 diagnosis between 1/1/2020 and 3/31/2022, 762 met our inclusion criteria. Of these, 300 (39.4%) identified as Hispanic (94% male, p &lt; 0.05) and 462 (60.6%) identified as non-Hispanic white (90% male). At baseline Hispanic patients were younger and were more likely to have underlying comorbidities (T2DM, mild liver disease, AIDS; p &lt; 0.05). Hispanic patients with cancer demonstrated hospitalization rates after the index COVID19 diagnosis comparable to those of non-Hispanic white patients at 30d (34% vs 28.8%) and 60d (35.7% vs 32%). Conclusions: Although Hispanic patients with cancer demonstrated a trend of higher hospitalization rates at 30d and 60d following a COVID19 diagnosis, this difference was not statistically significant. Further studies with a larger sample size and generalizability to assess ethnic disparities in other clinical settings are warranted.
The impact of high body mass index on hepatocellular carcinoma in viral hepatitis and cirrhosis.
Authors: Moonho Kim, Baek Gyu Jun, Hwang Sik Shin, Sang-Wook Yi
Journal: Journal of Clinical Oncology (2023)

10537 Background: The impact of a high body mass index (BMI) on hepatocellular carcinoma (HCC) has been unclear in patients with viral hepatitis and liver cirrhosis (LC). Our aim is to investigate the association between BMI and HCC in patients with viral hepatitis and LC. Methods: We conducted a population-based cohort study. In total, 156,143 Korean adults who underwent routine health examinations during 2003-2006 were followed up until December 2019 via linkage to the national hospital discharge records. Multivariable-adjusted hazard ratios (HRs) per each 5 kg/m 2 increase in BMI for HCC risk were calculated using Cox proportional hazard models. Results: BMI was positively associated with HCC in hepatitis B virus (HBV) patients with BMI range of 27.5~29.9 kg/m² and BMI ≥30 kg/m² and LC patients with a BMI range of 27.5~29.9 kg/m². Assuming linear associations, HRs were 1.17 for HBV, 1.08 for hepatitis C virus (HCV), and 1.11 for LC. In the subgroup analysis for the LC, the HR were 1.19 in the HBV-LC, 1.38 in the HCV-LC, 1.36 in the alcoholic LC, and 1.59 in the fatty liver LC. Women showed stronger positive associations between BMI and HCC than men in the HBV (HR, 1.45 vs 1.06) and LC (1.27 vs. 1.02). The positive association seemed to be stronger in younger age ( &lt; 65 years) in HBV (1.17 vs 1.05) and HCV (1.33 vs 0.77) patients compared to older age ( &gt; 65 years). Conclusions: In the BMI range of ≥25 kg/m 2 , a higher BMI was associated with an increased risk of HCC in LC and HBV. The associations between BMI and HCC risk are different according to liver disease, sex and age.
Real-world data of early-onset colorectal cancer (EOCRC) in private health system in Brazil.
Authors: Maria Cecilia Mathias, Mariana Gil, Thais Passarini, Jorge Alexandre Canedo, Eduardo Dias de Moraes, Sabrina Pereira, Larissa Costa Amorim, Gabriel Prolla, Laura Freitas, Matheus Costa e Silva, Carlos Gil Ferreira, Bruno Lemos Ferrari, Bernardo Garicochea, Rodrigo Dienstmann, Roberto Almeida Gil, Renata D'Alpino Peixoto, Alexandre A. Jácome
Journal: Journal of Clinical Oncology (2023)

e18830 Background: Incidence of EOCRC has been sharply rising in the past decades in the US and present distinct clinical features when compared to late-onset CRC (LOCRC). Data from other countries are scarce. We compared clinical characteristics and treatment outcomes between patients ≤45y (EOCRC) and ≥60y (LOCRC). Methods: Retrospective study evaluating 510 patients with metastatic CRC treated at 5 Oncoclinicas centers in Brazil from January 2011 to December 2019. Demographic and clinical data were retrieved from electronic medical records. The median OS was calculated by Kaplan-Meier method and prognostic factors were evaluated via multivariate Cox Regression. Results: After excluding patients with ages between 46 and 59, 326 patients were identified. EOCRC was represented by 72 (22%) patients (median age 39) and LOCRC by 254 patients (median age 69). Median follow-up was 39.5m, with 231 deaths (71%). Median OS was 33.2m and 40.9m (p = 0.971), respectively. Likewise, stage IV at presentation was similar between the groups (68% vs 58%, p = 0.215), as well as the rate of dMMR status (4% vs 6%, p = 0.655). Clinical and treatment characteristics that were statistically different between the groups are described in Table 1. In multivariate analysis, presence of RAS mutations (HR:1.83), and undergoing triplet chemotherapy (HR:3.24), primary tumor resection (HR:0.40), and metastasectomy (HR:0.45) were the variables with prognostic effect on OS. Conclusions: In this real-world analysis of mCRC, EOCRC patients presented similar clinical presentation and OS, but they were more aggressively treated compared to the older patients. There is no evidence that more intensive treatments translate into superior clinical outcomes in young patients. Real-world data have been progressively adopted in clinical practice and should be considered before clinical decision making in order to offer treatments with minimal toxicity burden to the patients. Future studies with larger population are needed and may bring additional data. [Table: see text]
Real-world utilization pattern of pexidartinib in patients with tenosynovial giant cell tumor (TGCT).
Authors: Feng Lin, Jenny Tse, Nazneen Fatima Shaikh, Elizabeth J Wang, Aimee Near, Jackie Kwong, William D. Tap
Journal: Journal of Clinical Oncology (2023)

e18792 Background: In August 2019, pexidartinib became the first and only systemic therapy approved in the US for the treatment of symptomatic TGCT associated with severe morbidity or functional limitations and not amenable to improvement with surgery. As TGCT is a rare condition, real-world pexidartinib utilization data are limited and greatly needed to inform clinical use. Methods: Adults (≥18 years) with ≥1 pexidartinib claim from 8/2/2019 - 1/1/2022 identified in the IQVIA Longitudinal Prescription Claims (LRx) linked with Medical Claims (Dx) databases were analyzed. Patient characteristics 6 months before first (index) pexidartinib claim were summarized. Pexidartinib dosing, interruption, and treatment duration were assessed post-index. Discontinuation was defined as treatment gap of ≥90 days. Results: Of 167 patients treated with pexidartinib (median age, 45 years; 59.9% female), the most common comorbidities were osteoarthritis (10.8%), hypertension (8.4%), and obesity (7.8%); 3 patients (1.8%) had mild liver disease and none had moderate-to-severe liver disease pre-index. Median starting dose was 800 mg/day (Table). Over median follow-up of 17.9 months, median (IQR) dose was 622 (442-750) mg/day. Dose reduction was observed in 57 (34.1%) patients and dose increase in 14 (8.4%) patients. Probability of discontinuation was 24.0%, 38.3% and 53.7% at 3-, 6- and 12-month follow-up, respectively. Median (95% confidence interval [CI]) time to pexidartinib discontinuation was 10.1 (7.1, 13.0) months. Fourteen (8.4%) patients restarted pexidartinib after discontinuation. Overall, median (95% CI) time on treatment was 14.2 (10.1, 18.4) months when treatment duration after restart of therapy was considered. Rate of joint surgery after pexidartinib treatment was low (7.2%, n = 12) with median (IQR) time from index to surgery of 5.4 (4.2-12.5) months and median time from last pexidartinib claim to surgery of 2.7 (1.0-8.2) months. Three patients restarted pexidartinib after surgery. Conclusions: Compared to patients in ENLIVEN, real-world pexidartinib patients had similar demographic characteristics. Real-world average daily dose was lower than the full dose recommended in the prescribing information. Compared to ENLIVEN, rate of discontinuation was higher in first 6 months but similar at 12 months. Findings could inform treatment strategy in clinical practice and future research in TGCT. [Table: see text]
Predicting immunotherapy outcomes of melanoma patients before treatment by intestinal microbiota.
Authors: Tao Wang, Yang Liu, Qian Wang, Shanshan Xiao
Journal: Journal of Clinical Oncology (2023)

e21512 Background: To find the appropriate patients that can benefit from immunotherapy is very important in precision medicine. Biomarkers such as PD-L1, TMB and MSI attracted a lot of attentions in immunotherapy. Emerging biomarkers such as intestinal microbiota and others also showed promising predictive/prognosis values in the clinical studies. The purpose of this study was to evaluate new prediction models based on intestinal microbiota data from the melanoma patients before treatment. Methods: This study enrolled 41 microbiome 16S patients from published datasets. Firstly, survival analysis was performed on the response group (R) and non-response group (NR). Species in all the samples were included to perform univariate cox regression analysis, and the differences of intestinal microbiota between R and NR groups were evaluated by alpha and beta diversities. The specific differential microbiota was obtained by Student's t-test and LEfSe and further screened by the best performance of the five machine learning methods. To find the minimum set of the microbiota species, logistic regression and ROC curve were used. Analyses of KO data included PCA, differential and KEGG enrichment analysis. Significantly enriched pathways were scored by ssGSEA. The spearman correlations between ssGSEA scores and final microbiota species were calculated. Results: The non-response (NR) group had shorter PFS. Univariate cox regression analysis on all the samples identified 36 species which might be associated with prognosis. Alpha and beta diversities showed the R and NR groups were significantly different. Student's t-test identified 52 differential species and LEfSe identified 72 OTUs. Most of the microbiome enriched in R group belonged to firmicutes, while those enriched in NR group belonged to firmicutes, bacteroides and proteobacteria. We then constructed five machine learning models to predict PD1 immunotherapy responses of the melanoma patients and finally identified 25 species. Logistic regression and ROC curve identified the minimum set of the microbiota species including methanobrevibacter smithii, schaalia odontolytica, cuneatibacter caecimuris. A total of 119 differentially expressed KO genes were obtained by differential analysis, and those genes were mainly enriched in 25 pathways. Spearman correlation analysis found that those three microbial species were mainly associated with 12 pathways which might influence the classification of the two groups. Conclusions: In this study we identified three microbial species which might contribute to the classification of the R and NR patient groups. The models based on intestinal microbiota data can predict immunotherapy outcomes of melanoma patients.
Effect of pandemic-related reductions in cancer care delivery on different US health systems.
Authors: Lauren M Janczewski, Joseph Cotler, Ryan P Merkow, Bryan E Palis, Heidi Nelson, Timothy Mullett, Daniel J Boffa
Journal: Journal of Clinical Oncology (2023)

e18811 Background: The Covid-19 pandemic caused unprecedented challenges in the diagnosis and evaluation of cancer. At the same time, cancer treatment was potentially impacted by significant constraints on patients and hospitals; however, the extent and differential influence on different hospital types is unknown. Our objective was to assess the patterns of treatment utilization to better characterize the impact of the first year of the Covid-19 pandemic on the US healthcare system. Methods: The National Cancer Database (NCDB) was queried for patients treated for any type of malignancy diagnosed from 2018-2020. Autoregressive models were used to forecast expected findings for 2020 based on observations from the prior two years. Descriptive univariate statistics using chi-squared tests were performed to compare observed-to-expected findings for treatment utilization and losses in provided care in 2020. Results: Overall, 1,229,654 patients underwent treatment for any newly diagnosed cancer in the NCDB in 2020, representing a 16.8% reduction compared to what was expected. Stratified by treatment modality, 146,805 fewer patients than expected underwent surgery, 80,480 fewer received radiation and 68,014 fewer received chemotherapy. Reductions in treatment were examined by hospital type. Academic hospitals experienced the greatest reduction in provided care (-105,093 patients, -19%) compared to community programs (-72,432 patients, -14%) and integrated networks (-40,827 patients, -13%). However, there were fewer hospitals in the academic cohort which exaggerated the impact on each hospital. Thus, academic hospitals lost approximately 484 patients per hospital while community hospitals lost 99 patients and integrated networks lost 110 patients per hospital. The losses in provided care were most dramatic in terms of surgical care, as academic hospitals operated on 314 fewer patients per hospital (-20%) than expected while community hospitals on average operated on 69 fewer patients (-16%) and integrated networks 71 fewer patients (-14%). Conclusions: The impact of the first year of the Covid-19 pandemic on cancer treatment was heterogenous, resulting in nearly twice the number of missed surgical patients, compared to other treatment modalities. While all hospital types were affected by the pandemic, cancer care at academic hospitals experienced disproportionate reductions, with each hospital losing more than 4 times the number of treated patients than other hospital types. Continued efforts to recover from the strain of the pandemic on the US healthcare system will need to consider the complex influence of treatment declines across hospital types and different cancer service lines.